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|Optimized Control Using Machine Learning (It’s About Time)
The good news is that if or when we develop this technology, we can once again eliminate the arduous task of line by line programming and squeeze every drop of energy waste out of buildings.
some of my contemporaries I was never a fan of pneumatic controls and
have no remorse for their passing. Sure I had a lot of contracts with
customers to periodically calibrate their systems and made good money
doing it, but I always knew full well that it was highly unlikely those
systems would stay calibrated till the next maintenance interval. In
fact, I would wager to say that some would not stay calibrated for more
than a few hours.
I was first introduced to the Andover Controls Sunkeeper back in the
late 1970s, I was hooked. The Sunkeeper was the first truly DDC
system I had seen that could replace pneumatics. Just add, what was
a blazing fast 300 baud modem, and you could remote into the building
through a dumb terminal. Want to add a couple of days’ battery backup?
Buy a Sears Die Hard car battery (I kid you not). The “drum”
programming was a bit awkward to learn but none the less if you were
smart and creative enough it could do things that no pneumatic system
could match. More importantly, it was far more accurate and reliable
than pneumatics and practically bullet proof.
was convinced that the Andover system was the end all be all when in
the early 1980s I was introduced to the Novar Controls system. Many may
not be aware, but Novar was the first building automation system that I
know of to incorporate self-learning control algorithms. The control algorithms
eliminated the need for PID loops and the manual tuning that goes along
with them. Not only were Novar’s control loops self-learning their
optimized start-stop program was as well. What took me days to program
in the Andover System took hours on the Novar and best of all, there
was no de-bugging needed. Because of its self-learning algorithms, the
Novar system was programmed via simple menu driven commands. No
programming language to learn, easy to use. Novar proved that the more intelligence you can put into the control method the easier it is to program and deploy.
was, however, one fallibility with the Novar system that is worth
mentioning. The self-learning algorithms were great as long as the
systems it was controlling were working correctly. On the other hand,
if there was a fault, all hell broke loose. For example, if a chiller
was running inefficiently and the cooling process was slower than
normal, you might find the optimized start program starting the
building up at mid-night. The conclusion being, if systems have a fault, advanced algorithms can make matters worse and waste more energy than systems without intelligence.
It has been more than three decades since Novar introduced self-learning algorithms for control loops in buildings and where are we today?
Building automation systems are still pretty complicated to program and deploy, and we continue to treat the control of buildings as a series of PID loops instead of what they are which is a unified, holistic system.
In the 1980s we started designing mostly variable speed buildings. One would think that by now automation systems would be able to automatically determine if it makes more sense to increase fan speed on the air handlers or reduce chilled water temperature, just as a simple example. Controlling systems holistically to get to the lowest energy usage is surprisingly missing.
It isn’t that we lack the knowledge, these types of machine learning algorithms have been around for decades and are always being improved and optimized. In the 1980s the microprocessors used in the early building automation systems had a fraction of the power of those today but artificial intelligence (AI) algorithms such as Simulated Annealing which probably could have solved this problem were. Now I don’t know if the microprocessors back then would have been powerful enough to run those algorithms but certainly the microprocessors we have can today. Furthermore, newer better-optimized machine learning algorithms such as Extended Compact Genetic Algorithms have gained favor over simulated annealing, and there is little doubt that modern microprocessors have the horsepower needed to run them…..but hold the fort.
As Novar discovered in the 1980s, if the machines you are trying to optimize are not performing correctly, machine learning can easily make things much worse. It is much like the adage that an accountant can make a mistake of several dollars whereas a computer can make a mistake of millions. This is a challenge to overcome.
seems that deploying an effective fault detection program is clearly
needed before implementing machine learning. The fault detection
then be tasked not only to make maintenance more efficient but also to
qualify whether the machine learning algorithms would and should be
allowed to execute. Without this precaution, I believe machine learning
as it pertains to optimizing building control has the potential to
cause more harm than good.
good news is that if or when we develop this technology, we can once
again eliminate the arduous task of line by line programming and
squeeze every drop of energy waste out of
About the Author
Pitcher is the CEO of Weber Sensors working on creating the next
generation of sensors for the HVAC industry. Mr. Pitcher was the
founder of Scientific Conservation one of the first cloud based fault
detection companies and has held many executive positions in his 45
plus years in the HVAC industry. He can be reached at email@example.com
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